exp1
Browse files- exp1/README.MD +20 -0
- exp1/events.out.tfevents.1765837464.39363d63bd9c.543.0 +3 -0
- exp1/events.out.tfevents.1765851571.939957212f6c.1781.0 +3 -0
- exp1/events.out.tfevents.1765918302.fa752da7519e.427.0 +3 -0
- exp1/events.out.tfevents.1765927954.fa752da7519e.964.0 +3 -0
- exp1/model_124000.pt +3 -0
- exp1/result.txt +64 -0
exp1/README.MD
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# HPA-RMVPE
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This repository hosts an experimental integration of the **HyperACE** architecture (YOLOv13) into the **RMVPE** pitch extractor.
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Despite being a test version, this model has achieved **excellent results**, reaching our highest Raw Pitch Accuracy (RPA) metrics to date.
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## 📂 Datasets
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Training was conducted using a combination of the following datasets to ensure timbral variety:
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* **MIR-1K**
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* **PTDB-TUG**
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* **VocalSet**
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## ⚠️ Training Disclaimer
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**Training was not continuous.** Due to operational constraints, the process was fragmented into multiple sessions (paused and resumed). This may have affected the learning rate scheduler and optimizer states, potentially preventing the model from reaching its absolute theoretical convergence. However, current performance is outstanding.
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## 🐛 Known Limitations
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* **Silence Instability:** The model currently exhibits minor instability (jitter or noise) during segments of absolute silence. This is expected due to the nature of HyperACE's global attention and will be addressed in future updates.
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exp1/events.out.tfevents.1765837464.39363d63bd9c.543.0
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version https://git-lfs.github.com/spec/v1
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exp1/events.out.tfevents.1765851571.939957212f6c.1781.0
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version https://git-lfs.github.com/spec/v1
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exp1/events.out.tfevents.1765918302.fa752da7519e.427.0
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version https://git-lfs.github.com/spec/v1
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size 1022470
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exp1/events.out.tfevents.1765927954.fa752da7519e.964.0
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version https://git-lfs.github.com/spec/v1
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size 1341569
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exp1/model_124000.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:3b6aea7f32a39be1bca545e0e55bf1b740da092cfde19bb0d6bff142778382da
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size 251547922
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exp1/result.txt
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2000 0.8175 0.8481 0.8722 0.9558 0.1707 2.8267 0.2086
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| 2 |
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4000 0.8780 0.9001 0.8637 0.9735 0.2932 3.7266 0.1533
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| 3 |
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6000 0.9056 0.9189 0.8938 0.9842 0.2673 3.7147 0.1120
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| 4 |
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8000 0.9191 0.9258 0.9012 0.9851 0.2518 3.2124 0.1119
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10000 0.9251 0.9309 0.8991 0.9878 0.2578 3.4243 0.0844
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| 6 |
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12000 0.9256 0.9317 0.9080 0.9855 0.2479 2.6636 0.0931
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14000 0.9235 0.9293 0.9085 0.9874 0.2380 2.7625 0.0933
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| 8 |
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16000 0.9327 0.9384 0.9034 0.9869 0.2535 2.7503 0.0868
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2000 0.8200 0.8354 0.8698 0.9710 0.2182 3.2188 0.1435
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| 10 |
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18000 0.9333 0.9387 0.9080 0.9875 0.2529 2.7239 0.0821
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| 11 |
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20000 0.9368 0.9416 0.9035 0.9878 0.2529 2.8772 0.0837
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| 12 |
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22000 0.9351 0.9403 0.8996 0.9885 0.2711 2.6640 0.0682
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24000 0.9319 0.9371 0.8953 0.9874 0.2857 2.7429 0.0821
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26000 0.9336 0.9392 0.8856 0.9876 0.3170 2.6247 0.0585
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28000 0.9330 0.9381 0.9017 0.9860 0.2629 2.6403 0.0921
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| 16 |
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30000 0.9362 0.9410 0.9045 0.9869 0.2677 2.5407 0.0850
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| 17 |
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32000 0.9359 0.9406 0.9033 0.9867 0.2843 2.6399 0.0833
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| 18 |
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34000 0.9374 0.9427 0.9004 0.9886 0.2615 2.7939 0.0734
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36000 0.9368 0.9416 0.9061 0.9868 0.2673 2.6438 0.0863
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38000 0.9346 0.9394 0.8985 0.9878 0.2620 2.5882 0.0788
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40000 0.9404 0.9451 0.9019 0.9883 0.2826 2.5002 0.0705
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42000 0.9378 0.9430 0.8976 0.9881 0.2719 2.4825 0.0740
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44000 0.9387 0.9433 0.8987 0.9872 0.2799 2.4780 0.0767
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46000 0.9358 0.9405 0.9086 0.9867 0.2601 2.4505 0.0821
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48000 0.9395 0.9438 0.9101 0.9877 0.2404 2.3840 0.0786
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50000 0.9409 0.9458 0.8983 0.9882 0.2726 2.5979 0.0645
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52000 0.9398 0.9443 0.9094 0.9865 0.2475 2.4211 0.0827
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| 28 |
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54000 0.9413 0.9456 0.8966 0.9881 0.2868 2.6218 0.0676
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| 29 |
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56000 0.9430 0.9474 0.8930 0.9881 0.2639 2.7399 0.0752
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| 30 |
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58000 0.9400 0.9443 0.9005 0.9876 0.2605 2.5161 0.0815
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60000 0.9407 0.9449 0.9094 0.9879 0.2372 2.3675 0.0817
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62000 0.9425 0.9470 0.9048 0.9889 0.2540 2.4354 0.0681
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64000 0.9426 0.9470 0.9140 0.9881 0.2297 2.4932 0.0840
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66000 0.9436 0.9478 0.8987 0.9887 0.2772 2.5033 0.0645
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| 35 |
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68000 0.9434 0.9475 0.8736 0.9875 0.2928 2.8165 0.0831
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| 36 |
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70000 0.9433 0.9475 0.9134 0.9884 0.2238 2.4103 0.0769
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| 37 |
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72000 0.9425 0.9465 0.8903 0.9887 0.2435 2.4401 0.0737
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74000 0.9417 0.9462 0.9141 0.9866 0.2253 2.3796 0.0998
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76000 0.9428 0.9471 0.9116 0.9867 0.2318 2.5023 0.0951
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78000 0.9430 0.9474 0.9046 0.9881 0.2200 2.4283 0.0837
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80000 0.9434 0.9478 0.9150 0.9874 0.2166 2.3845 0.0827
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| 42 |
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82000 0.9445 0.9486 0.9179 0.9879 0.2284 2.4458 0.0767
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84000 0.9426 0.9468 0.9214 0.9862 0.2055 2.3229 0.0895
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86000 0.9435 0.9477 0.9154 0.9881 0.2339 2.3859 0.0701
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| 45 |
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88000 0.9440 0.9482 0.9181 0.9877 0.2156 2.3501 0.0794
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90000 0.9441 0.9483 0.9096 0.9885 0.2242 2.4832 0.0735
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92000 0.9445 0.9486 0.9060 0.9883 0.2389 2.4062 0.0686
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94000 0.9437 0.9480 0.9130 0.9880 0.2164 2.3602 0.0777
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96000 0.9445 0.9487 0.9148 0.9872 0.2241 2.3689 0.0867
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98000 0.9454 0.9494 0.9139 0.9871 0.2124 2.3209 0.0906
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100000 0.9443 0.9486 0.9151 0.9867 0.2177 2.3754 0.0968
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102000 0.9446 0.9488 0.9224 0.9870 0.2056 2.2541 0.0884
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104000 0.9440 0.9482 0.9165 0.9867 0.2144 2.3476 0.0892
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106000 0.9446 0.9486 0.9133 0.9877 0.2341 2.3982 0.0797
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108000 0.9440 0.9480 0.9189 0.9866 0.2124 2.3219 0.0901
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110000 0.9449 0.9491 0.9050 0.9875 0.2101 2.4535 0.0846
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112000 0.9441 0.9483 0.9137 0.9876 0.2226 2.3523 0.0813
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114000 0.9452 0.9493 0.9094 0.9872 0.2294 2.4485 0.0815
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116000 0.9454 0.9494 0.9217 0.9876 0.2081 2.3372 0.0835
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118000 0.9455 0.9495 0.9184 0.9874 0.2001 2.3435 0.0848
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120000 0.9450 0.9493 0.9129 0.9877 0.2336 2.4190 0.0777
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122000 0.9439 0.9479 0.8939 0.9869 0.2475 2.4601 0.0865
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| 63 |
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124000 0.9461 0.9501 0.9190 0.9879 0.2275 2.3223 0.0743
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| 64 |
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126000 0.9456 0.9497 0.9164 0.9877 0.2081 2.3438 0.0826
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